Read historical prices of stock indices
https://finance.yahoo.com/world-indices
R code
The following R code retrieves historical daily prices of selected stock indices given their symbols as of 2022-08-14.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | #========================================================# # Quantitative ALM, Financial Econometrics & Derivatives # ML/DL using R, Python, Tensorflow by Sang-Heon Lee # # https://shleeai.blogspot.com #--------------------------------------------------------# # read historical prices of stock indices #========================================================# graphics.off(); rm(list = ls()) library(quantmod) library(stringr) # trim #------------------------------------------------- # Symbols of stock indices, as of 2022-08-14 #------------------------------------------------- vstr_symbol <- " Symbol , Name ^GSPC , S&P 500 ^DJI , Dow 30 ^IXIC , Nasdaq ^NYA , NYSE COMPOSITE (DJ) ^XAX , NYSE AMEX COMPOSITE INDEX ^BUK100P , Cboe UK 100 ^RUT , Russell 2000 ^VIX , CBOE Volatility Index ^FTSE , FTSE 100 ^GDAXI , DAX PERFORMANCE-INDEX ^FCHI , CAC 40 ^STOXX50E , ESTX 50 PR.EUR ^N100 , Euronext 100 Index ^BFX , BEL 20 ^N225 , Nikkei 225 ^HSI , HANG SENG INDEX 000001.SS , SSE Composite Index 399001.SZ , Shenzhen Index ^STI , STI Index ^AXJO , S&P/ASX 200 ^AORD , ALL ORDINARIES ^BSESN , S&P BSE SENSEX ^JKSE , Jakarta Composite Index ^KLSE , FTSE Bursa Malaysia KLCI ^NZ50 , S&P/NZX 50 INDEX GROSS ^KS11 , KOSPI Composite Index ^TWII , TSEC weighted index ^GSPTSE , S&P/TSX Composite index ^BVSP , IBOVESPA ^MXX , IPC MEXICO ^TA125.TA , TA-125 ^JN0U.JO , Top 40 USD Net TRI Index " #------------------------------------------- # split symbols and make vector #------------------------------------------- df <- read.table(text = str_trim(vstr_symbol), sep = ",", header = TRUE) df <- as.data.frame(df); df df$Symbol <- str_trim(gsub("[\t\r\n,]", "", df$Symbol)) df$Name <- str_trim(gsub("[\t\r\n,]", "", df$Name)) df nc <- nrow(df) # number of index #------------------------------------------- # read price information #------------------------------------------- sdate <- as.Date("2001-01-01") edate <- as.Date("2022-08-12") getSymbols(df$Symbol, from=sdate, to=edate) #------------------------------------------- # collect only adjusted prices #------------------------------------------- price <- NULL for(i in 1:nc) { eval(parse(text=paste0( "price <- cbind(price,`", gsub("\\^","",df$Symbol[i]),"`[,6])"))) } # modify column Name as only symbol colnames(price) <- gsub(".Adjusted", "", colnames(price)) # convert to data.frame with the first column as Date df.price <- cbind(time=time(price), as.data.frame(price)) rownames(df.price) <- NULL # partial selection of complete cases # by S&P 500, Nikkei 225, HANG SENG INDEX df.price <- df.price[complete.cases( df.price[,c("GSPC","N225","HSI")]),] #------------------------------------------- # print time series of daily prices #------------------------------------------- head(df.price,3) tail(df.price,3) | cs |
Running the above R code displays the status of data reading process as follows.
Finally, we can get the collection of individual stock indices.
2023-11-29) In the section '# collect only adjusted prices', I received helpful feedback that using lapply is more efficient than employing eval(parse(...)) within a for loop for the same purpose. I wholeheartedly agree with this suggestion.
# ============================================== # Revised: collect only adjusted prices ShortSymbol <- gsub("\\^","",df$Symbol) price <- NULL dfResult = lapply(ShortSymbol, function(x) { return(get(x)[,6]) }) # Combine the partial Result price <- do.call(cbind,dfResult) # ============================================== | cs |
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